Random Processes

Random Processes is the graduate-level bridge between probability theory and the noisy, time-varying signals you actually encounter in communications, control, and signal processing — the course where you stop treating randomness as a single variable and start reasoning about whole trajectories indexed by time. You'll work through ten problem sets and two midterms building fluency with correlation and spectral tools, then applying them to canonical models (Poisson, Wiener, Markov, shot noise) and to LTI systems driven by stochastic inputs. It's the analytical foundation that later courses on detection, estimation, information theory, and stochastic control quietly assume you already have.